@inproceedings{wang-etal-2019-extracting,
title = "Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers",
author = "Wang, Haoyu and
Tan, Ming and
Yu, Mo and
Chang, Shiyu and
Wang, Dakuo and
Xu, Kun and
Guo, Xiaoxiao and
Potdar, Saloni",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1132",
doi = "10.18653/v1/P19-1132",
pages = "1371--1377",
abstract = "Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.",
}
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<abstract>Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.</abstract>
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%0 Conference Proceedings
%T Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
%A Wang, Haoyu
%A Tan, Ming
%A Yu, Mo
%A Chang, Shiyu
%A Wang, Dakuo
%A Xu, Kun
%A Guo, Xiaoxiao
%A Potdar, Saloni
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-etal-2019-extracting
%X Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.
%R 10.18653/v1/P19-1132
%U https://aclanthology.org/P19-1132
%U https://doi.org/10.18653/v1/P19-1132
%P 1371-1377
Markdown (Informal)
[Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers](https://aclanthology.org/P19-1132) (Wang et al., ACL 2019)
ACL
- Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, and Saloni Potdar. 2019. Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1371–1377, Florence, Italy. Association for Computational Linguistics.